WikiHowQA: A comprehensive benchmark for multi-document non-factoid question answering
V Bolotova-Baranova, V Blinov… - Proceedings of the …, 2023 - aclanthology.org
Answering non-factoid questions (NFQA) is a challenging task, requiring passage-level
answers that are difficult to construct and evaluate. Search engines may provide a summary …
answers that are difficult to construct and evaluate. Search engines may provide a summary …
Arabicaqa: A comprehensive dataset for arabic question answering
In this paper, we address the significant gap in Arabic natural language processing (NLP)
resources by introducing ArabicaQA, the first large-scale dataset for machine reading …
resources by introducing ArabicaQA, the first large-scale dataset for machine reading …
Using large language models to generate, validate, and apply user intent taxonomies
Log data can reveal valuable information about how users interact with web search services,
what they want, and how satisfied they are. However, analyzing user intents in log data is …
what they want, and how satisfied they are. However, analyzing user intents in log data is …
An intent taxonomy of legal case retrieval
Legal case retrieval is a special Information Retrieval (IR) task focusing on legal case
documents. Depending on the downstream tasks of the retrieved case documents, users' …
documents. Depending on the downstream tasks of the retrieved case documents, users' …
Narrative why-question answering: A review of challenges and datasets
E Kalbaliyev, K Sirts - Proceedings of the 2nd Workshop on …, 2022 - aclanthology.org
Abstract Narrative Why-Question Answering is an important task to assess the causal
reasoning ability of systems in narrative settings. Further progress in this domain needs …
reasoning ability of systems in narrative settings. Further progress in this domain needs …
Effective contrastive weighting for dense query expansion
Verbatim queries submitted to search engines often do not sufficiently describe the user's
search intent. Pseudo-relevance feedback (PRF) techniques, which modify a …
search intent. Pseudo-relevance feedback (PRF) techniques, which modify a …
GRM: generative relevance modeling using relevance-aware sample estimation for document retrieval
Recent studies show that Generative Relevance Feedback (GRF), using text generated by
Large Language Models (LLMs), can enhance the effectiveness of query expansion …
Large Language Models (LLMs), can enhance the effectiveness of query expansion …
Retrieval-based Evaluation for LLMs: A Case Study in Korean Legal QA
While large language models (LLMs) have demonstrated significant capabilities in text
generation, their utilization in areas requiring domain-specific expertise, such as law, must …
generation, their utilization in areas requiring domain-specific expertise, such as law, must …
ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering
Question Answering (QA) effectively evaluates language models' reasoning and knowledge
depth. While QA datasets are plentiful in areas like general domain and biomedicine …
depth. While QA datasets are plentiful in areas like general domain and biomedicine …
Ms-shift: An analysis of ms marco distribution shifts on neural retrieval
Abstract Pre-trained Language Models have recently emerged in Information Retrieval as
providing the backbone of a new generation of neural systems that outperform traditional …
providing the backbone of a new generation of neural systems that outperform traditional …